search for: factr

Hi, I am using optim with a L-BFGS-B method to minimize a function. As I've understood, the way to specify a tolerance for stopping optimization is through "factr" argument. My function, is by construction, minimal when equal to 1. I wonder if there is any way to pass this info to "optim". If not, how "factr" argument works (I am quite confused about the relationship between this argument and the Machine eps). Please, could someone g...

..., in OPTIM, I've got the following message. --------------------------------------------------------------------- $par [1] 0.176166426835580 $value [1] 1322.17600079332 $counts function gradient 8 8 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [1,] 46300.3853279247 --------------------------------------------------------------------- First, what does that message, "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH", mean? and I am wondering if the estimates are reliable. Any co...

hi all i dont understand the error message that is produced by the optim function. can anybody help??? ie: [[1]]$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" can anyone help? ########################################################################### SK.FIT(XDATA=a,XDATAname="a",PHI1=1,v=5,vlo=2,vhi=300,phi2lo=.01) [[1]] [[1]]$par [1] -0.01377906 0.83859445 0.34675230 300.00000000 [[1]]$value [1] 90.59185 [[1]]$counts...

....040113 22.7 factor(group)3 2.372638 0.040184 59.0 capacity:factor(group)2 -0.173956 0.003606 -48.2 capacity:factor(group)3 -0.380799 0.003631 -104.9 Correlation of Fixed Effects: (Intr) capcty fct()2 fct()3 cp:()2 capacity -0.322 factr(grp)2 -0.709 0.228 factr(grp)3 -0.708 0.228 0.502 cpcty:fc()2 0.235 -0.730 -0.309 -0.166 cpcty:fc()3 0.233 -0.725 -0.165 -0.304 0.529 ----------- Results 3 ----------- Generalized linear mixed model fit by the Laplace approximation Formula: entrypr...

...od="L-BFGS-B", lower=rep(0, 3), upper=rep(1, 3)) D1.unbound $par c1 c2 c3 0.004387706 0.203562156 0.300825550 $value [1] 0.07811152 $counts function gradient 8 8 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" Any suggestion on how to fix the error "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"? [[alternative HTML version deleted]]

...eturn result; } void grad(int n, double *par, double *gr, void *ex) { gr[0] = 2*par[0]; gr[1] = 2*par[1]; } int main(void) { int n = 2; int m = 5; double init[] = {2,3}; double lower[] = {-100, -100}; double upper[] = {100, 100}; int nbd[] = {0, 0}; double Fmin; int fail; void *ex = 0; double factr = 1e7; double pgtol = 0; int fncount; int grcount; int maxit = 10; char msg[1000]; int trace = 0; int nREPORT = 10; /* from http://cran.r-project.org/doc/manuals/R-exts.html#Optimization void lbfgsb(int n, int lmm, double *x, double *lower, double *upper, int *n...

...x)-k)^2 return(r) } gr <- function(x) { n <- length(x) r <- (b^(0:(n-1)))*(1/x) - 4000000*(sum(x)-k) return(r) } nvar <- 10 (sols <- optim(rep(20,nvar),f,gr,method="L-BFGS-B",lower=rep(0,nvar),upper=rep(k,nvar),control=list(fnscale=-1,parscale=rep(2000,nvar),factr=1e-300,pgtol=1e-300)))

...Class Mode IP 0 mle list NE 0 mle list I don't get the output I would have, i.e. the one from > summary(f$IP) summary(f$IP) Maximum likelihood estimation Call: mle(minuslogl = IPNeglogPoisL, method = "L-BFGS-B", fixed = list(), control = list(maxit = 1e+08, factr = 1e-20)) Coefficients: Estimate Std. Error a 1242.0185506 44.92341097 b 0.8802538 0.01685811 -2 log L: 145.3509 What I want to do is something like: AICs <- AIC(logLik(f)) and then have all the AICs in the vector AICs. It must be possible or is this again a namespace issue? R...

...ues the minimisation. It stops and gives a (-log(likelihood))=6104.45, with the messages: "there are 50 or more warnings" ( warnings() = "multi-arguments returns are deprecated" in a function used by the program) $convergence=0 $message= CONVERGENCE:REL_REDUCTION_OF_F <= FACTR*EPSMCH What does it mean? Is the convergence reached? What can it be concluded from these two steps? Thank you very much for your advices and help.

...,upper = rep(Inf, 2), hessian=TRUE, control=list(trace=1)) iter 0 value 3.011784 final value 2.802694 converged $par [1] 4.6597779 0.3860387 $value [1] 2.802694 $counts function gradient 17 17 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" WHich gives an estimate of stDev = 0.38 while the empirical stDev = 1.94 Is there anything wrong above in the code? Thanks in advance

Can anyone help me with lbfgsb (function for optimization)? It takes the following parameters: void lbfgsb (int n, int lmm, double *x, double *lower, double *upper, int *nbd, double *Fmin, optimfn fn, optimgr gr, int *fail, void *ex, double factr, double pgtol, int *fncount, int *grcount, int maxit, char *msg, int trace, int nREPORT); What do I put for parameter ex (11th parameter)? I looked at optim.c codes at R sites and it's a structure that has bunch of objects such as SEXP R_fcall, SEXP R_gcall, SEXP R_env, double* ndeps, etc. I...

...ally, everything works fine, but for some simulations, the algorithm just gets stuck. When I let my function (+gradient) evaluation print out the function value, I see that L-BFGS-B keeps calling the function with arguments giving the same function value (up to at least the accuracy required by factr, i.e. 1E-8). So, I don't really understand why it doesn't stop. Is this a well-known problem? The main problem is that the optimization doesn't stop (at least not in reasonable time) and so the whole simulation gets stuck. Is there a way to control the time used by "L-BFGS-B&qu...

...r,Linn,hessian=TRUE, method=c("L-BFGS-B"),control = list(trace=1,abstol=0.001),lower=c(0,0,0,0,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf),upper=c(1,1,1,1,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf)) #nlminb package estimate<-nlminb(init.par,Linn,gr=NULL,hessian=TRUE,control = list(trace=1,factr=1),lower=c(0,0,0,0,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf),upper=c(1,1,1,1,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf)) I did not get same results from above equations. Log-likelihood values are close but parameter estimation completely different. My expectation is very close to "nlminb&quot...

...Winf k t0 b sigma [1] 24.27206813 0.04679844 0.00100000 1.61760492 0.01000000 $value [1] -11.69524 $counts function gradient 143 143 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [,2] [,3] [,4] [,5] [1,] 1.867150e+00 1.262763e+03 -7.857719 -5.153276e+01 -1.492850e-05 [2,] 1.262763e+03 8.608461e+05 -5512.469266 -3.562137e+04 9.693180e-05 [3,] -7.857719e+00 -5.512469e+03 41.670222 2.47316...

...and using these as starting values in the next function call. The final call to optim() returns the following: $par [1] 0.2272361 0.8037642 26.8591998 3.0631280 0.2224566 $value [1] -46.13906 $counts function gradient 4 4 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [,2] [,3] [,4] [,5] [1,] 1.267070e+17 1.012691e+17 1.348054e+15 625551.58724 9.359559e+07 [2,] 1.012691e+17 8.189877e+16 1.144248e+15 569562.44945 8.699072e+07 [3,] 1.348054e+15 1.144248e+15 2.457323e+05 3426.60293 -2.297009e+03 [4,] 6.255516e+05 5.695624e+05 3.426603e+03...

...ambda = lambda) The output is: $par [1] 0.56350964 0.56350964 0.56350964 0.56350964 0.00000000 -0.29515957 [7] 0.00569937 0.32543297 0.18615880 $value [1] 0.2529198 $counts function gradient 31 31 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" Warning messages: 1: bounds can only be used with method L-BFGS-B in: optim(par, errorFunction, gr = NULL, method = "Nelder-Mead", 2: NAs introduced by coercion If I change my "error-function" to errorFunction <- function(localShifts,globalShift,fileName,e...

...or(sex)m 0.581713 0.296229 1.9637 0.0495616 * ResCondCorp -0.176251 0.020263 -8.6982 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) fct()1 fctr(g) fctr(s) factr(pct)1 -0.334 fctr(gcmp)j -0.417 0.066 factor(sx)m -0.505 -0.002 -0.173 ResCondCorp -0.309 -0.010 0.302 -0.032 ### Here is the error message: ### Warning message: Estimated variance for factor 'factor(cdrgsaou2$ids)' is effectively zero...

...0.3 8.5 --- Val = 42.70603 ... Eval fn at 0.7425713 21.12820 0.001 --- Val = 64.99 Eval fn at 0.7425713 21.12920 0.002 --- Val = 60.20449 Eval fn at 0.7425713 21.12920 0.001 --- Val = 64.99 > o$val [1] 64.99 > o$convergence [1] 0 > o$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" So optim thinks it has found an optimum (i.e. minimum). But my initial guess is better than optim's answer; and optim has visited many points which are better than its final answer. If I choose a different initial guess, like c(.7,10.3,1), optim reaches the answer I expect. What...

...n.log $par [1] 0.007369536 0.032623958 1.025064715 0.315420992 0.288083186 0.008728551 [7] 1.016895527 0.978822785 0.552299864 1.016390800 0.000100000 $value [1] -1697.267 $counts function gradient 50 50 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" ########### Here ``likeli.time.log'' and ``gradient.time.log'' are functions, ``temp.censor'' is a list with input variables for the two functions (data, fixed parameters, etc.), and the parameter space is of dimension 11. Note that value returned is clearly...

...fine: > optRes <- optim(c(0,0), method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan, + gr=IRT.gradZL, + lower=c(-Inf, -Inf), upper=c(Inf, Inf), t=st, X=sx) > optRes $par [1] -0.6975157 0.7944972 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" Does anyone have an idea what this could be, and what I could try to avoid this error? I tried bounding the parameters, with lower=c(-10, -10) and upper=... but that made no difference. Thanks, Diederik Roijers Utrecht University MSc student. ------ PS: the other functions I am us...